{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would become impractical as we move to bigger models. Ideally we want to build networks using a more modular design so that we can implement different layer types in isolation and then snap them together into models with different architectures.\n",
    "\n",
    "In this exercise we will implement fully-connected networks using a more modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive derivative of loss with respect to outputs and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "\n",
    "In addition to implementing fully-connected networks of arbitrary depth, we will also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run the following from the cs231n directory and try again:\n",
      "python setup.py build_ext --inplace\n",
      "You may also need to restart your iPython kernel\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('y_train: ', (49000,))\n",
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('y_test: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "  print(('%s: ' % k, v.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: foward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.76984772881e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 1e-9.\n",
    "print('Testing affine_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  5.39910036865e-11\n",
      "dw error:  9.9042118654e-11\n",
      "db error:  2.41228675681e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around 1e-10\n",
    "print('Testing affine_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.99999979802e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be around 5e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ReLU layer: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1  2  3]\n",
      "[0 2 3]\n",
      "[-1  2  3]\n"
     ]
    }
   ],
   "source": [
    "# Numpy passes by reference so you must use .copy() \n",
    "\n",
    "a = np.array([-1,2,3])\n",
    "print(a)\n",
    "b = a.copy()\n",
    "b[b<0] = 0\n",
    "print(b)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.27563491363e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be around 3e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward:\n",
      "dx error:  6.7505621216e-11\n",
      "dw error:  8.16201557044e-11\n",
      "db error:  7.82672402146e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "print('Testing affine_relu_forward:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "You implemented these loss functions in the last assignment, so we'll give them to you for free here. You should still make sure you understand how they work by looking at the implementations in `cs231n/layers.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.9996027491\n",
      "dx error:  1.40215660067e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.3025458445\n",
      "dx error:  9.38467316199e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be 1e-9\n",
    "print('Testing svm_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be 2.3 and dx error should be 1e-8\n",
    "print('\\nTesting softmax_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Two-layer network\n",
    "In the previous assignment you implemented a two-layer neural network in a single monolithic class. Now that you have implemented modular versions of the necessary layers, you will reimplement the two layer network using these modular implementations.\n",
    "\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. This class will serve as a model for the other networks you will implement in this assignment, so read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.52e-08\n",
      "W2 relative error: 3.48e-10\n",
      "b1 relative error: 6.55e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 8.18e-07\n",
      "W2 relative error: 2.85e-08\n",
      "b1 relative error: 1.09e-09\n",
      "b2 relative error: 7.76e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "for reg in [0.0, 0.7]:\n",
    "  print('Running numeric gradient check with reg = ', reg)\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solver\n",
    "In the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.\n",
    "\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves at least `50%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "(Iteration 1 / 4900) loss: 2.316765\n",
      "(Epoch 0 / 10) train acc: 0.164000; val_acc: 0.134000\n",
      "(Epoch 1 / 10) train acc: 0.441000; val_acc: 0.456000\n",
      "(Epoch 2 / 10) train acc: 0.485000; val_acc: 0.477000\n",
      "(Iteration 1001 / 4900) loss: 1.479779\n",
      "(Epoch 3 / 10) train acc: 0.485000; val_acc: 0.486000\n",
      "(Epoch 4 / 10) train acc: 0.489000; val_acc: 0.500000\n",
      "(Iteration 2001 / 4900) loss: 1.456327\n",
      "(Epoch 5 / 10) train acc: 0.524000; val_acc: 0.487000\n",
      "(Epoch 6 / 10) train acc: 0.556000; val_acc: 0.505000\n",
      "(Iteration 3001 / 4900) loss: 1.219321\n",
      "(Epoch 7 / 10) train acc: 0.548000; val_acc: 0.482000\n",
      "(Epoch 8 / 10) train acc: 0.547000; val_acc: 0.493000\n",
      "(Iteration 4001 / 4900) loss: 1.210687\n",
      "(Epoch 9 / 10) train acc: 0.592000; val_acc: 0.520000\n",
      "(Epoch 10 / 10) train acc: 0.581000; val_acc: 0.495000\n"
     ]
    }
   ],
   "source": [
    "model = TwoLayerNet()\n",
    "solver = None\n",
    "\n",
    "print()\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves at least  #\n",
    "# 50% accuracy on the validation set.                                        #\n",
    "##############################################################################\n",
    "model = TwoLayerNet(hidden_dim=100, reg=0.1)\n",
    "\n",
    "solver = Solver(model, data, \n",
    "               update_rule='sgd',\n",
    "               optim_config={\n",
    "                   'learning_rate': 1e-3,\n",
    "               },\n",
    "               lr_decay=0.95,\n",
    "               num_epochs=10, batch_size=100,\n",
    "               print_every=1000)\n",
    "solver.train()\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7snokEREREdH8x6DNoF2VGOOmNEYN9IiIiIiIqDc5l/xfaNpRiZEpjUREREREFIYzbQZR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3gJs52Crteo1n8cjn1xnUMaF0FK/n8DwI0AZovIMQD3ATAAQCn1DwD+P5jl/l+FWfL/tyu1sERE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\nYuqfZYUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ee818e2358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multilayer network\n",
    "Next you will implement a fully-connected network with an arbitrary number of hidden layers.\n",
    "\n",
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the initialization, the forward pass, and the backward pass. For the moment don't worry about implementing dropout or batch normalization; we will add those features soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial loss and gradient check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. Do the initial losses seem reasonable?\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-6 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.29827743056\n",
      "W1 relative error: 1.27e-07\n",
      "W2 relative error: 1.13e-06\n",
      "W3 relative error: 3.36e-08\n",
      "b1 relative error: 1.57e-08\n",
      "b2 relative error: 2.61e-09\n",
      "b3 relative error: 1.30e-10\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.01416697664\n",
      "W1 relative error: 1.42e-07\n",
      "W2 relative error: 8.92e-08\n",
      "W3 relative error: 1.65e-08\n",
      "b1 relative error: 2.89e-08\n",
      "b2 relative error: 6.50e-09\n",
      "b3 relative error: 2.98e-10\n"
     ]
    }
   ],
   "source": [
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "  print('Running check with reg = ', reg)\n",
    "  model = FullyConnectedNet([H1, H2], input_dim=D, num_classes=C,\n",
    "                            reg=reg, weight_scale=5e-2, dtype=np.float64)\n",
    "\n",
    "  loss, grads = model.loss(X, y)\n",
    "  print('Initial loss: ', loss)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another sanity check, make sure you can overfit a small dataset of 50 images. First we will try a three-layer network with 100 units in each hidden layer. You will need to tweak the learning rate and initialization scale, but you should be able to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 11.443226\n",
      "(Epoch 0 / 20) train acc: 0.420000; val_acc: 0.121000\n",
      "(Epoch 1 / 20) train acc: 0.420000; val_acc: 0.139000\n",
      "(Epoch 2 / 20) train acc: 0.500000; val_acc: 0.121000\n",
      "(Epoch 3 / 20) train acc: 0.780000; val_acc: 0.142000\n",
      "(Epoch 4 / 20) train acc: 0.680000; val_acc: 0.151000\n",
      "(Epoch 5 / 20) train acc: 0.800000; val_acc: 0.160000\n",
      "(Iteration 11 / 40) loss: 0.918910\n",
      "(Epoch 6 / 20) train acc: 0.940000; val_acc: 0.167000\n",
      "(Epoch 7 / 20) train acc: 0.940000; val_acc: 0.164000\n",
      "(Epoch 8 / 20) train acc: 0.980000; val_acc: 0.167000\n",
      "(Epoch 9 / 20) train acc: 1.000000; val_acc: 0.166000\n",
      "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.166000\n",
      "(Iteration 21 / 40) loss: 0.020589\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.168000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.169000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.169000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.169000\n",
      "(Iteration 31 / 40) loss: 0.002467\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.169000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.170000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.169000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.168000\n"
     ]
    },
    {
     "data": {
      "image/png": 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JY5O8sqrunC8GMwAAVuGqZWzz/Um+KckHuvuzVXV1kqetbVkAAJvbJUNYd5+dBK/HV1WS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\n27rlNm3LWXe10j7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ee85229b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "weight_scale = 3.9e-2\n",
    "learning_rate = 2e-3\n",
    "model = FullyConnectedNet([100, 100],\n",
    "              weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to use a five-layer network with 100 units on each layer to overfit 50 training examples. Again you will have to adjust the learning rate and weight initialization, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 124.278659\n",
      "(Epoch 0 / 20) train acc: 0.120000; val_acc: 0.105000\n",
      "(Epoch 1 / 20) train acc: 0.260000; val_acc: 0.112000\n",
      "(Epoch 2 / 20) train acc: 0.260000; val_acc: 0.113000\n",
      "(Epoch 3 / 20) train acc: 0.420000; val_acc: 0.126000\n",
      "(Epoch 4 / 20) train acc: 0.580000; val_acc: 0.115000\n",
      "(Epoch 5 / 20) train acc: 0.640000; val_acc: 0.131000\n",
      "(Iteration 11 / 40) loss: 14.995054\n",
      "(Epoch 6 / 20) train acc: 0.760000; val_acc: 0.140000\n",
      "(Epoch 7 / 20) train acc: 0.820000; val_acc: 0.121000\n",
      "(Epoch 8 / 20) train acc: 0.880000; val_acc: 0.133000\n",
      "(Epoch 9 / 20) train acc: 0.980000; val_acc: 0.136000\n",
      "(Epoch 10 / 20) train acc: 0.980000; val_acc: 0.136000\n",
      "(Iteration 21 / 40) loss: 0.311285\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.140000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.141000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.140000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.140000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.140000\n",
      "(Iteration 31 / 40) loss: 0.001926\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.139000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.140000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.140000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.140000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.139000\n"
     ]
    },
    {
     "data": {
      "image/png": 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/BFuARYY4YZ3t3X/43nB2wrG7j2fv/sMzqmhjOBFsF44eS+dzwXbfwYVZlwYw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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ee851c2d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "learning_rate = 4e-04\n",
    "weight_scale = 1e-01\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, small_data,\n",
    "                print_every=10, num_epochs=20, batch_size=25,\n",
    "                update_rule='sgd',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Inline question: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer net vs training the five layer net?\n",
    "\n",
    "# Answer:\n",
    "Harder to train the deeper 5 layer net, was harder to find correct hyperparameters. Extremely sensitive to learning rate and weight decay choice.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than 1e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.88234703351e-09\n",
      "velocity error:  4.26928774328e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-3, 'velocity': v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(next_w, expected_next_w))\n",
    "print('velocity error: ', rel_error(expected_velocity, config['velocity']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.711571\n",
      "(Epoch 0 / 5) train acc: 0.113000; val_acc: 0.107000\n",
      "(Iteration 11 / 200) loss: 2.238236\n",
      "(Iteration 21 / 200) loss: 2.106421\n",
      "(Iteration 31 / 200) loss: 2.009745\n",
      "(Epoch 1 / 5) train acc: 0.252000; val_acc: 0.228000\n",
      "(Iteration 41 / 200) loss: 2.071934\n",
      "(Iteration 51 / 200) loss: 2.000329\n",
      "(Iteration 61 / 200) loss: 2.000558\n",
      "(Iteration 71 / 200) loss: 2.032896\n",
      "(Epoch 2 / 5) train acc: 0.305000; val_acc: 0.239000\n",
      "(Iteration 81 / 200) loss: 1.787015\n",
      "(Iteration 91 / 200) loss: 1.948045\n",
      "(Iteration 101 / 200) loss: 1.908183\n",
      "(Iteration 111 / 200) loss: 1.790272\n",
      "(Epoch 3 / 5) train acc: 0.362000; val_acc: 0.318000\n",
      "(Iteration 121 / 200) loss: 1.622508\n",
      "(Iteration 131 / 200) loss: 1.626411\n",
      "(Iteration 141 / 200) loss: 1.657006\n",
      "(Iteration 151 / 200) loss: 1.605388\n",
      "(Epoch 4 / 5) train acc: 0.374000; val_acc: 0.326000\n",
      "(Iteration 161 / 200) loss: 1.793911\n",
      "(Iteration 171 / 200) loss: 1.702514\n",
      "(Iteration 181 / 200) loss: 1.641009\n",
      "(Iteration 191 / 200) loss: 1.893227\n",
      "(Epoch 5 / 5) train acc: 0.440000; val_acc: 0.335000\n",
      "\n",
      "running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 2.700714\n",
      "(Epoch 0 / 5) train acc: 0.107000; val_acc: 0.101000\n",
      "(Iteration 11 / 200) loss: 2.099349\n",
      "(Iteration 21 / 200) loss: 2.087603\n",
      "(Iteration 31 / 200) loss: 1.949566\n",
      "(Epoch 1 / 5) train acc: 0.314000; val_acc: 0.265000\n",
      "(Iteration 41 / 200) loss: 1.878286\n",
      "(Iteration 51 / 200) loss: 1.863341\n",
      "(Iteration 61 / 200) loss: 1.768134\n",
      "(Iteration 71 / 200) loss: 1.692800\n",
      "(Epoch 2 / 5) train acc: 0.387000; val_acc: 0.318000\n",
      "(Iteration 81 / 200) loss: 1.685225\n",
      "(Iteration 91 / 200) loss: 1.699404\n",
      "(Iteration 101 / 200) loss: 1.596376\n",
      "(Iteration 111 / 200) loss: 1.538974\n",
      "(Epoch 3 / 5) train acc: 0.432000; val_acc: 0.333000\n",
      "(Iteration 121 / 200) loss: 1.534038\n",
      "(Iteration 131 / 200) loss: 1.369632\n",
      "(Iteration 141 / 200) loss: 1.552820\n",
      "(Iteration 151 / 200) loss: 1.430205\n",
      "(Epoch 4 / 5) train acc: 0.453000; val_acc: 0.336000\n",
      "(Iteration 161 / 200) loss: 1.686754\n",
      "(Iteration 171 / 200) loss: 1.577135\n",
      "(Iteration 181 / 200) loss: 1.423417\n",
      "(Iteration 191 / 200) loss: 1.510579\n",
      "(Epoch 5 / 5) train acc: 0.521000; val_acc: 0.365000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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AiIvbrQewvtjHISIiIiIiInOBVLkkIiIiIiKi6DGgIyIiIiIiSigGdERERERERAnFgI6I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\nkiRJkpSjLHSSJEmSlKP+D3DFo9H1FhPcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ee852d8438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': 1e-2,\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.52468751104e-08\n",
      "cache error:  2.64779558072e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation; you should see errors less than 1e-7\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  1.13956917985e-07\n",
      "v error:  4.20831403811e-09\n",
      "m error:  4.21496319311e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation; you should see errors around 1e-7 or less\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "running with  adam\n",
      "(Iteration 1 / 200) loss: 3.106747\n",
      "(Epoch 0 / 5) train acc: 0.115000; val_acc: 0.091000\n",
      "(Iteration 11 / 200) loss: 2.210265\n",
      "(Iteration 21 / 200) loss: 1.956287\n",
      "(Iteration 31 / 200) loss: 1.756120\n",
      "(Epoch 1 / 5) train acc: 0.360000; val_acc: 0.291000\n",
      "(Iteration 41 / 200) loss: 1.842754\n",
      "(Iteration 51 / 200) loss: 1.781894\n",
      "(Iteration 61 / 200) loss: 1.761059\n",
      "(Iteration 71 / 200) loss: 1.753436\n",
      "(Epoch 2 / 5) train acc: 0.426000; val_acc: 0.355000\n",
      "(Iteration 81 / 200) loss: 1.513254\n",
      "(Iteration 91 / 200) loss: 1.499052\n",
      "(Iteration 101 / 200) loss: 1.355281\n",
      "(Iteration 111 / 200) loss: 1.496890\n",
      "(Epoch 3 / 5) train acc: 0.516000; val_acc: 0.365000\n",
      "(Iteration 121 / 200) loss: 1.392661\n",
      "(Iteration 131 / 200) loss: 1.673347\n",
      "(Iteration 141 / 200) loss: 1.164107\n",
      "(Iteration 151 / 200) loss: 1.345863\n",
      "(Epoch 4 / 5) train acc: 0.547000; val_acc: 0.345000\n",
      "(Iteration 161 / 200) loss: 1.452306\n",
      "(Iteration 171 / 200) loss: 1.406066\n",
      "(Iteration 181 / 200) loss: 1.462187\n",
      "(Iteration 191 / 200) loss: 1.538535\n",
      "(Epoch 5 / 5) train acc: 0.562000; val_acc: 0.384000\n",
      "\n",
      "running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.529507\n",
      "(Epoch 0 / 5) train acc: 0.150000; val_acc: 0.117000\n",
      "(Iteration 11 / 200) loss: 2.199356\n",
      "(Iteration 21 / 200) loss: 1.889404\n",
      "(Iteration 31 / 200) loss: 1.705612\n",
      "(Epoch 1 / 5) train acc: 0.377000; val_acc: 0.331000\n",
      "(Iteration 41 / 200) loss: 1.911895\n",
      "(Iteration 51 / 200) loss: 1.950610\n",
      "(Iteration 61 / 200) loss: 1.712061\n",
      "(Iteration 71 / 200) loss: 1.601992\n",
      "(Epoch 2 / 5) train acc: 0.450000; val_acc: 0.333000\n",
      "(Iteration 81 / 200) loss: 1.480468\n",
      "(Iteration 91 / 200) loss: 1.488455\n",
      "(Iteration 101 / 200) loss: 1.711671\n",
      "(Iteration 111 / 200) loss: 1.676743\n",
      "(Epoch 3 / 5) train acc: 0.469000; val_acc: 0.349000\n",
      "(Iteration 121 / 200) loss: 1.387414\n",
      "(Iteration 131 / 200) loss: 1.426662\n",
      "(Iteration 141 / 200) loss: 1.416300\n",
      "(Iteration 151 / 200) loss: 1.366775\n",
      "(Epoch 4 / 5) train acc: 0.509000; val_acc: 0.341000\n",
      "(Iteration 161 / 200) loss: 1.302790\n",
      "(Iteration 171 / 200) loss: 1.341278\n",
      "(Iteration 181 / 200) loss: 1.382770\n",
      "(Iteration 191 / 200) loss: 1.427042\n",
      "(Epoch 5 / 5) train acc: 0.545000; val_acc: 0.360000\n",
      "\n"
     ]
    },
    {
     "data": {
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x4kSys7PbelkdKnPuXHLuWYo7NxeMwZ2bS849S8mcO7fT27Jq1SrGjh3L+PHjefvttwPD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\nvaROnUrR1q2nbHxS1fqM9UxdOpXskmweHv0w1/e7/rRdLuUkSgogZW3l+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DII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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ee85c1bba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "  print('running with ', update_rule)\n",
    "  model = FullyConnectedNet([100, 100, 100, 100, 100], weight_scale=5e-2)\n",
    "\n",
    "  solver = Solver(model, small_data,\n",
    "                  num_epochs=5, batch_size=100,\n",
    "                  update_rule=update_rule,\n",
    "                  optim_config={\n",
    "                    'learning_rate': learning_rates[update_rule]\n",
    "                  },\n",
    "                  verbose=True)\n",
    "  solvers[update_rule] = solver\n",
    "  solver.train()\n",
    "  print()\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.title('Training accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.title('Validation accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in list(solvers.items()):\n",
    "  plt.subplot(3, 1, 1)\n",
    "  plt.plot(solver.loss_history, 'o', label=update_rule)\n",
    "  \n",
    "  plt.subplot(3, 1, 2)\n",
    "  plt.plot(solver.train_acc_history, '-o', label=update_rule)\n",
    "\n",
    "  plt.subplot(3, 1, 3)\n",
    "  plt.plot(solver.val_acc_history, '-o', label=update_rule)\n",
    "  \n",
    "for i in [1, 2, 3]:\n",
    "  plt.subplot(3, 1, i)\n",
    "  plt.legend(loc='upper center', ncol=4)\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a good model!\n",
    "Train the best fully-connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully-connected net.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional nets rather than fully-connected nets.\n",
    "\n",
    "You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 1960) loss: 2.310381\n",
      "(Epoch 0 / 10) train acc: 0.107000; val_acc: 0.110000\n",
      "(Iteration 101 / 1960) loss: 1.575998\n",
      "(Epoch 1 / 10) train acc: 0.455000; val_acc: 0.455000\n",
      "(Iteration 201 / 1960) loss: 1.600110\n",
      "(Iteration 301 / 1960) loss: 1.399041\n",
      "(Epoch 2 / 10) train acc: 0.518000; val_acc: 0.478000\n",
      "(Iteration 401 / 1960) loss: 1.490020\n",
      "(Iteration 501 / 1960) loss: 1.355560\n",
      "(Epoch 3 / 10) train acc: 0.555000; val_acc: 0.494000\n",
      "(Iteration 601 / 1960) loss: 1.174710\n",
      "(Iteration 701 / 1960) loss: 1.319703\n",
      "(Epoch 4 / 10) train acc: 0.551000; val_acc: 0.511000\n",
      "(Iteration 801 / 1960) loss: 1.250983\n",
      "(Iteration 901 / 1960) loss: 1.174454\n",
      "(Epoch 5 / 10) train acc: 0.584000; val_acc: 0.502000\n",
      "(Iteration 1001 / 1960) loss: 1.136036\n",
      "(Iteration 1101 / 1960) loss: 1.149657\n",
      "(Epoch 6 / 10) train acc: 0.602000; val_acc: 0.516000\n",
      "(Iteration 1201 / 1960) loss: 1.176249\n",
      "(Iteration 1301 / 1960) loss: 1.286951\n",
      "(Epoch 7 / 10) train acc: 0.606000; val_acc: 0.516000\n",
      "(Iteration 1401 / 1960) loss: 1.239997\n",
      "(Iteration 1501 / 1960) loss: 1.136515\n",
      "(Epoch 8 / 10) train acc: 0.594000; val_acc: 0.526000\n",
      "(Iteration 1601 / 1960) loss: 1.089136\n",
      "(Iteration 1701 / 1960) loss: 1.056335\n",
      "(Epoch 9 / 10) train acc: 0.647000; val_acc: 0.530000\n",
      "(Iteration 1801 / 1960) loss: 1.056623\n",
      "(Iteration 1901 / 1960) loss: 0.982376\n",
      "(Epoch 10 / 10) train acc: 0.636000; val_acc: 0.530000\n"
     ]
    }
   ],
   "source": [
    "best_model = None\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# batch normalization and dropout useful. Store your best model in the         #\n",
    "# best_model variable.                                                         #\n",
    "################################################################################\n",
    "\n",
    "learning_rate = 3.113669e-04 \n",
    "weight_scale = 2.461858e-02\n",
    "model = FullyConnectedNet([100, 100, 100, 100],\n",
    "                weight_scale=weight_scale, dtype=np.float64)\n",
    "solver = Solver(model, data,\n",
    "                print_every=100, num_epochs=10, batch_size=250,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': learning_rate,\n",
    "                }\n",
    "         )\n",
    "solver.train()\n",
    "\n",
    "best_model = model\n",
    "\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test you model\n",
    "Run your best model on the validation and test sets. You should achieve above 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.53\n",
      "Test set accuracy:  0.506\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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